Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
Table of Contents (24 chapters)
1
Section 1: Reinforcement Learning Foundations
7
Section 2: Deep Reinforcement Learning
12
Section 3: Advanced Topics in RL
17
Section 4: Applications of RL

Detecting cyberattacks in a smart grid

Smart cities, by definition, run on intense digital communications between its assets. Besides its benefits, this makes smart cities prone to cyberattacks. As reinforcement learning is finding its way into cybersecurity, in this section, we describe how it can be applied to detecting attacks on a smart power grid infrastructure. Throughout the chapter, we follow the model proposed in (Kurt et al. 2019), while leaving the details to the paper.

Let's start with describing the power grid environment.

The problem of early detection of cyberattacks in a power grid

An electricity power grid consists of nodes, called buses, which correspond to generation, demand, or power line intersection points. Grid authorities collect measurements from these buses to make certain decisions such as brining in additional power generation units. To this end, a critical quantity measured is the phase angle at each bus (except the reference bus), which makes...